Head, AI Product & Governance (Risk and CFCR)

Job Summary

•    This role will sit at the heart of our AI transformation — shaping strategy, enabling adoption, and bridging business needs with the Bank’s technology and AI capabilities. You’ll work directly with Head, Services and Transformation, CFCR & AI and partner closely with AI experts, Tech, Data, and business teams across the Bank.
•    This is an opportunity for someone who wants to grow their career in AI, influence how Risk and CFCR functions evolve, and take a frontline role in enabling responsible and scaled adoption of AI in a critical part of the Bank.
•    To support the build-out of the AI Strategy and AI Target Operating Model for CFCR & Risk, and to act as a business enabler who connects functional needs with technology and AI delivery teams. The role ensures clarity of priority use cases, alignment across stakeholders, and effective mobilisation of AI initiatives.  

Key Responsibilities

Strategy (AI Strategy & Operating Model Development)
•    Help shape the AI strategic roadmap for CFCR/Risk, including use-case prioritisation, capability build, and value articulation.
•    Support design and documentation of the AI Target Operating Model, covering governance, processes, roles, skills, and adoption frameworks.
•    Benchmark internal and external practices to ensure the function stays aligned with global AI advancements.
Business - (Business–Technology Interface)
•    Act as the bridge between Risk/CFCR teams and AI/Tech delivery groups, ensuring clarity of requirements and alignment of expectations.
•    Translate business needs into structured AI problem statements and help refine solution options.
•    Track progress, unblock issues, and support sprint planning or delivery rituals where required.
Processes - (AI Enablement & Stakeholder Engagement)
•    Facilitate workshops, discovery sessions, and capability building initiatives to raise AI literacy.
•    Partner with SMEs, AI & model experts, and product teams to shape high quality use-case documentation and readiness materials.
•    Drive clear communication, helping senior stakeholders stay informed on progress, decisions, and value delivered.
•    Facilitate cross-functional teams to work on complex risk and compliance challenges.
People & Talent
•    Recruit individuals with the necessary skills and expertise in AI, data science, risk management, and compliance.
•    Invest in continuous learning and development programs to keep the team updated on the latest trends, technologies, and regulatory changes.
•    Foster a culture of risk awareness and proactive risk management within the team.

•    Develop a succession plan to ensure continuity in risk management leadership and expertise.
•    Address any work-related stress or challenges that may impact the team's ability to manage risks effectively.
•    Identify and mentor potential future leaders within the team.
•    Build AI literacy across the organization, ensuring employees understand the limitations and risks of the tools they use.
•    Promote accountability where teams feel empowered to report potential hazards or unexpected model behavior.
Risk Management
•    Conduct comprehensive risk assessments to identify potential risks associated with AI and data products.
•    Develop mitigation strategies to address identified risks, including operational, reputational, and compliance risks.
•    Promote the ethical use of AI, ensuring transparency, fairness, and accountability in AI models and decisions.
•    Establish guidelines to prevent bias and discrimination in AI algorithms.
•    Maintain human oversight for high-stakes decisions (e.g., SAR filings) to prevent AI errors.
•    Protect AI models from data poisoning, theft, and adversarial attacks through red-teaming and secure-by-design methodologies.
•    Proactively monitor and comply with evolving global AI regulations (e.g., EU AI Act, US regulatory guidance).
•    Encourage collaboration between the AI team and other departments, such as compliance, legal, and cybersecurity, to ensure a holistic approach to risk management.

Skills and Experience

•     Python, R
•     Data Engineering (SQL, ETL)
•     Cloud Platforms (AWS, AZURE)

Qualifications

Education:
•    Graduate
Training
•    AI/ML Fundamentals (Neural Networks, Nlp, Computer Vision)
Membership
•    PMP, AGILE, PROGRAMMING (PREFERRED)
Languages
•    (PYTHON, R), Data Engineering (SQL, ETL), and Cloud Platforms (AWS, AZURE) (Preferred)